Integration of INS, GPS, Magnetometer and Barometer for Improving Accuracy Navigation of the Vehicle

Sokolović, Vlada S.; Dikić, Goran; Stancic, Rade

doi:10.14429/dsj.63.4534

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…For attitude error damping, Madgwick et al [10] used the negative gradient between the axis of the gyroscopes and the magnetometer. Sokolovic et al used gain coefficients for gyros drift-damping error compensation [11]. Moreover, the magnetometer was used for the initial attitude determination and for the INS heading angle correction, e.g.…”

Section: • Specific Methods Of Gyroscope Drift Compensation By Using Pmentioning

confidence: 99%

“…The control signals at the horizontal channel, which are shown in Fig. 3, are In existing solutions [6] and [11], these values were used for the auto-correction of the horizontal channel errors, while in the solution proposed here , these values are used in order to compute the velocity vector of movement in the horizontal plane , which can be decomposed into the north and east components of velocity by using the yaw angle. This way, we can determine: In order to achieve more effective error damping in case of GPS data outage, the higher values of coefficients K are necessary.…”

Section: Fig 2 the North Channel Error Diagram Of Ins With Error Dmentioning

confidence: 99%

See 1 more Smart Citation

INS/GPS Navigation System Based on MEMS Technologies

Sokolović¹,

Dikić²,

Marković

et al. 2015

SV-JME

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Integrated navigation systems are widely used in all aspects of life, i.e. in various civilian and military applications. In the previous two decades, most of the attention in this area has been directed toward the use of low-cost sensors, which are characterized by the low accuracy. Therefore, many algorithms have been developed to implement inertial sensor errors damping, which are based on the integration of inertial sensors with the external sources of information (GPS receivers, magnetometers, barometers, etc.) or mathematical models developed for the self-damping of inertial sensors errors. This paper proposes a specific method of gyroscope drift compensation by using a PI controller based on the magnetometer measurements, as well as a method of errors compensation in the horizontal channel of navigation system by using the adaptive control signals. The experimental results obtained for the proposed solutions presented in this paper confirmed the possibility of the successful application of these solutions in the real-world environment.

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Section: • Specific Methods Of Gyroscope Drift Compensation By Using Pmentioning

confidence: 99%

Section: Fig 2 the North Channel Error Diagram Of Ins With Error Dmentioning

confidence: 99%

INS/GPS Navigation System Based on MEMS Technologies

Sokolović¹,

Dikić²,

Marković

et al. 2015

SV-JME

View full text Add to dashboard Cite

show abstract

“…The Ziegler-Nichols method [33], the critical proportionality method [34] and the attenuation curve method [35] are commonly used in engineering for tuning. Choosing a suitable method for PID tuning can also help improve the quality of control [36,37].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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For the application of the autonomous guidance of a quadrotor from confined undulant ground, terrain-following is the major issue for flying at a low altitude. This study has modified the open-source autopilot based on the integration of a multi-sensor receiver (a Global Navigation Satellite System (GNSS)), a Lidar-lite (a laser-range-finder device), a barometer and a low-cost inertial navigation system (INS)). These automatically control the position, attitude and height (a constant clearance above the ground) to allow terrain-following and avoid obstacles based on multi-sensors that maintain a constant height above flat ground or with obstacles. The INS/Lidar-lite integration is applied for the attitude and the height stabilization, respectively. The height control is made by the combination of an extended Kalman filter (EKF) estimator and a cascade proportional-integral-derivative (PID) controller that is designed appropriately for the noise characteristics of low accuracy sensors. The proposed terrain-following is tested by both simulations and real-world experiments. The results indicate that the quadrotor can continuously navigate and avoid obstacles at a real-time response of reliable height control with the adjustment time of the cascade PID controller improving over 50% than that of the PID controller.

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“…4,5 INS can be integrated with magnetometer information to effectively suppress the divergence error of vehicle heading to improve the accuracy of vehicle navigation systems. 5–7…”

Section: Introductionmentioning

confidence: 99%

Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

Cui

Wang

et al. 2021

Measurement and Control

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The integrated INS/magnetometer measurement is widely used in low-cost navigation systems. The integration has proven more effective in suppressing the divergence of heading than relying solely on a magnetometer because this is susceptible to local magnetic field interference, reducing heading accuracy. Magnetometers sense the local magnetic field that may be interfered by the nearby ferromagnetic material or strong electric currents. Hence, the magnetometer must be calibrated in the vehicle before use. When a magnetometer is installed near power components (engines, etc.), soft iron interference can be ignored. In the vehicle’s external environment, the time-varying hard iron interference can reach 100 times the strength of the geomagnetic field, meaning that a magnetometer cannot function efficiently because its accuracy is so reduced. Hence, the constant hard magnetic interference inside the vehicle is mainly concerned in this paper. An INS/Magnetometer heading estimation algorithm based on a two-stage Kalman filter is proposed to solve the problem by combining inertial sensor and magnetometer with attitude information. In the first stage filter, the constant hard iron interference is estimated by setting upward standing the three IMU axes. In the second stage filter, the INS/Magnetometer heading estimation is implemented. Finally, the results show that the algorithm improves the accuracy of vehicle heading calculations.

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Integration of INS, GPS, Magnetometer and Barometer for Improving Accuracy Navigation of the Vehicle

Cited by 17 publications

References 9 publications

INS/GPS Navigation System Based on MEMS Technologies

INS/GPS Navigation System Based on MEMS Technologies

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

Vehicle heading estimation of INS/magnetometer integrated system based on constant hard iron interference calibration

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